Method for mapping nfc field strength and location on mobile devices

ABSTRACT

Various embodiments are directed to a method and system for mapping or visualizing the magnetic fields and their associated field strengths of an object, such as a mobile computing device. An example source of the magnetic fields may be a near-field communication (NFC) reader configured in the object. A computer vision system or device may track a visual marker arranged near or on a magnetic field strength detector in order to associate, match, or map the magnetic field strength measurement readings of the detector at different positions or locations on the object. The computer vision system may generate and display a heat map of the object based on at least the magnetic field strength measurements and their relative positions.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 16/725,600, titled “METHOD FOR MAPPING NFC FIELD STRENGTH AND LOCATION ON MOBILE DEVICES” filed on Dec. 23, 2019. The contents of the aforementioned application are incorporated herein by reference in their entirety.

BACKGROUND

Near-field communication (NFC) is a communication protocol that enables two electronic components, one of which may be a portable device such as a smartphone, to establish communication by bringing them within close proximity of each other. NFC readers or coils, however, are typically arranged in different places on different mobile devices, which results in varying NFC performance characteristics. Accordingly, there is a need to locate and visualize the most active areas of NFC readers on NFC enabled mobile device and other types of contactless readers.

SUMMARY

Various embodiments are directed to a method and system for mapping or visualizing the magnetic fields and their associated field strengths of an object, such as a mobile computing device. An example source of the magnetic fields may be a near-field communication (NFC) reader configured in the object. A computer vision system or device may track a visual marker arranged near or on a magnetic field strength detector in order to associate, match, or map the magnetic field strength measurement readings of the detector at different positions or locations on the object. The computer vision system may generate and display a heat map of the object based on at least the magnetic field strength measurements and their relative positions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example magnetic field strength mapping system in accordance with one or more embodiments.

FIG. 2 illustrates an example magnetic field strength detector in accordance with one or more embodiments.

FIG. 3 illustrates an example heat map in accordance with one or more embodiments.

FIG. 4 illustrates another example of a magnetic field strength mapping system in accordance with one or more embodiments.

FIG. 5 illustrates an example flow diagram in accordance with one or more embodiments.

FIG. 6 illustrates another example of a flow diagram in accordance with one or more embodiments.

FIG. 7 illustrates an example computing architecture of a computing device in accordance with one or more embodiments.

FIG. 8 illustrates an example communications architecture in accordance with one or more embodiments.

DETAILED DESCRIPTION

Various embodiments are generally directed to a method and system for measuring magnetic field strengths at various locations on a mobile computing device and visualizing or visually displaying the measured magnetic field strengths on a heat map. The term “heat map” may be understood to broadly mean a graphical representation of data, e.g., measurement intensity, in the form of a map or diagram in which different data values can be represented in different colors.

In one embodiment, a magnetic field strength detection device or detector may include at least a magnetometer. The magnetic field strength detector may detect magnetic field strengths, e.g., NFC field strengths, at various locations on a mobile computing device, such as a smartphone, laptop, tablet computer, etc., using the magnetometer. In examples, one or more visual markers may be arranged on or adjacent relative to the detector, e.g., near or on the magnetometer, which can be tracked by a computer vision system while measurements are being taken. The NFC field strength readings may be provided to the vision system, which may be used to create a heat map of the magnetic or NFC field strengths measured at the various locations on the mobile computing device. As will be further described below, the heat map may be drawn or displayed over a digital image of the mobile computing device.

In another embodiment, the magnetic field strength detector may include one or more different types of sensors, such as a hall effect sensor, a coil, etc. The one or more sensors may be used to output different voltage values at various locations on the mobile computing device based on different levels of NFC or magnetic oscillations detected by the one or more sensors. In examples, the voltage values may be used to determine respective magnetic or NFC field strength measurements at the measured locations. A computer vision system may be used to track one or more visual markers relative to the detector and produce a heat map of the magnetic field strengths.

According to embodiments, the magnetic field strength detector may be a wand-like device or otherwise may have a wand-like shape. For instance, the tip of the wand-like device may be formed by a magnetometer, a sensor, or the like, which may be coupled or connected to processing circuitry that may be housed in a handle portion of the wand-like device. In examples, one or more visual markers trackable by a computer vision system may be arranged at the tip of the wand-like device. Moreover, the magnetic field strength detector may communicate, wired or wirelessly, the measured magnetic field strength values to the computer vision system for visualization of the measured values.

In one example previous solution, a magnetic field viewing film was used to show stationary or slowly changing magnetic fields of a magnetic source. The viewing film, however, may not possess the sensitivity needed to accurately map the magnetic field strengths originating from various portions of an electronic object. The embodiments and examples described herein overcome the problems existing in previous solutions by generating accurate visualizations, e.g., heat maps, of the object that indicate at least the location, source, and strength of magnetic fields emanating from the object, such as magnetic fields from an NFC reader configured in a smartphone. Accordingly, the locations and the most active areas of magnetic field sources on or within a particular object can be quickly and accurately identified.

Reference is now made to the drawings, where like reference numerals are used to refer to like elements throughout. In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding thereof. It may be evident, however, that the novel embodiments can be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form to facilitate a description thereof. The intention is to cover all modification, equivalents, and alternatives within the scope of the claims.

FIG. 1 illustrates an example magnetic field strength mapping system 100 according to embodiments. As shown, the mapping system 100 may include at least a magnetic field strength detector 101, a computer vision device 111, and a mobile device 120. The magnetic field strength detector 101 may be used to detect various magnetic field strengths at different positions on the mobile device 120, which can be visualized by the computer vision device 111 by tracking one or more visual markers on the magnetic field strength detector 101.

For example, the magnetic field strength detector 101 may include at least one or more magnetometers 102, processing circuitry 104, memory 106, and one or visual markers 108. It may be understood that a magnetometer may be any device, sensor, or hardware component that measures magnetic fields (e.g., flux density, field direction, field strength, and/or relative change(s) in the magnetic field at a particular location, etc.). The processing circuitry 104 may be any processing mechanism, e.g., hardware (central processing unit, processor, ASIC, field programmable gate array, or the like), software, etc., and along with the memory 106, may facilitate and perform processing of magnetic field measurements taken by the one or more magnetometers 102. In examples, the one or more visual markers 106 may be arranged anywhere on the detector 101 so that the computer vision device 111 may be able to “watch” and track the marker(s) 106 so as to associate a specific magnetic field measurement value with a specific measured position on the mobile device 120. As will be further described below, in some examples, a visual marker may be arranged on the magnetometer itself. Moreover, while not shown, it may be understood that the magnetic field strength detector 101 may further include interface components for allowing the detector 101 to communicate at least the measurement values with the computer vision device 111.

As further shown in FIG. 1, the computer vision device 111 may include at least one or more cameras 112, processing circuitry 114, memory 116, and a display 118. Moreover, it may be understood that the computer vision device may also include interface components for communicating with or receiving from the detector 101 magnetic field measurements or measurement values. In examples, the one or more cameras 112 may be digital, analog, visible-light cameras, or any suitable image capturing devices for acquiring image data, which can be in the form of multi-image or video sequences or in the form of multi-dimensional image data if more than one camera is implemented. The camera(s) 112 may track the one or more visual markers 108 at each specific position and determine whether there is a corresponding magnetic field strength measurement associated with that position. As will be further described below, using the processing circuitry 114 and executable programs stored in memory 116, the computer vision device 111 may visualize, map, or “draw” a heat map of the various magnetic field strength measurements over an image of the mobile device 120 (which may have also been taken by the cameras 112) based at least in part on the tracking of the visual marker(s) and the associated measurements.

The mobile device 120 may be any type of mobile computing device, such as a smartphone, a tablet computer, a wearable computer, a laptop, or the like. As illustrated, an example source of the magnetic field may be an NFC reader 122 (and other related NFC components, for example, an NFC reader coil). For example, a visualization (e.g., heat map) of the magnetic field strength measurements of the mobile device 120 by the detector 101 and the computer vision device 111 may reveal that the strongest magnetic field measurements are detected toward the top of the mobile device 120 (where the NFC reader 122 is located). Thus, advantageously, a heat map of the field strength measurements may visually indicate to a user that the “sweet spot” of the NFC reader is at the top-middle portion of the mobile device 120.

It may be understood that the illustrated magnetic field strength mapping system 100 and the components therein in FIG. 1 have been simplified for ease of explanation and are non-limiting examples. As such, there may more (or less) components than the components illustrated in the magnetic field strength detector 101, computer vision device 111, and the mobile device 120 of FIG. 1. Moreover, it may be understood that the computer vision device 111 and the components therein may be part of a computer vision system, which may include other computer vision devices and components connected over a network.

FIG. 2 illustrates an example magnetic field strength detector 200 according to embodiments. As described above, the magnetic field strength detector 200 may include at least a magnetometer 202 and processing circuitry and memory 204, which may be coupled by one or more wires 206. At least the processing circuitry and memory 204 may be arranged in a housing 208. As shown, the magnetic field strength detector 200 may generally have a wand-like shape, where the magnetometer 202 may form the tip of the wand and the housing 208 may form the handle. In examples, the wires 206 may be configured in a rigid manner (e.g., the wires themselves may be rigid, the wires may be twisted together to form a rigid structure, the wires may be encased in a rigid housing) so that the wire configuration can sufficiently hold or accommodate the weight of the magnetometer 202. In at least that regard, the wires 206 may form the general structure of the elongated body of the wand.

As further illustrated in FIG. 2, a visual marker 210 may be arranged on the magnetometer 202. The visual marker may be any type of visual aid to indicate a position, location, place, etc. as long as the marker is trackable by one or more cameras of a computer vision system or device. While the visual marker 210 arranged on the magnetometer 202 is a black circular dot, it may be understood that it may be any shape, color, colored shape, colored dot, visual pattern, and the like. For example, it may be star-shaped, triangular, or rectangular. In some examples, the visual marker may be machine-readable code that can be detected by a computer vision system for at least rendering information to the user, for instance, in augmented reality.

According to embodiments, a user may wave, hover, or scan the tip of the detector 200, e.g., the magnetometer 202, over the outer surface of a mobile device. At a first position relative to the mobile device, the magnetometer 202 may measure a first magnetic field strength. Similarly, the magnetometer 202 may measure a second magnetic field strength at a second position relative to the mobile device, may measure a third magnetic field strength at a third position, a fourth magnetic field strength at a fourth potion, and so on. The computer vision device, such as the computer vision device 111 of FIG. 1, may track the visual maker 210 when the first magnetic field strength is measured at the first position and receive the first magnetic field strength measurement from the detector 200 (not necessarily in any particular order). When the second magnetic field strength is measured at the second position, the visual marker 210 is tracked and the measurement is received by the computer vision device. The same may apply for the third position, the fourth position, etc.

In some examples, the magnetic field strength detector 200 may be configured to determine the source of the magnetic field(s) based on the signal frequencies originating from the source, and upon determination of that source, provide such information to the computer vision device. For example, if detected signal frequency is between 13 and 14 MHz, the detector 200 may determine that the signal is NFC. If the signal frequency is between 1850 and 1990 MHz, the source of such signal may be cellular.

Thereafter, the computer vision device may generate a heat map of the mobile device based on the magnetic field strength measurements (e.g., first field strength measurement, second field strength measurement, third measurement, fourth measurement). As will be described below, the heat map may visually indicate the magnetic field strengths at the respective measured locations on the mobile device and may further indicate—in color or in other ways—degrees of field strength, location relative to other measurements, type of signal being emitted from the mobile device, source of the measured fields, etc.

FIG. 3 illustrates an example heat map 300 of various magnetic fields and their respective strengths detected on a mobile device, such as a smartphone, according to embodiments. As shown, the heat map 300 may be overlaid on one or more digital images of the smartphone, such as a front image 304 and a rear image 306 of the smartphone. From the front side of the smartphone, the heat map 300 may indicate the presence of a magnetic field 308 on the top-left portion. The line representing the inner-most ring of the magnetic field 308 may be color-coded (e.g., red), or as shown, drawn thicker than the other lines to indicate that the magnetic field is the strongest in that region. As the magnetic field 308 expands outward from the inner-most portion, the field strength may be weaker, as indicated by the thinner lines representing the middle and outer-most rings (or may be color-coded in orange and yellow, respectively). Further shown is a generally large magnetic field 310, which may be very weak as indicated by dashed lines (or otherwise indicated by a color representing very weak field strength, such as blue).

From the rear side of the smartphone, the magnetic fields 308 and 310 may be indicated, represented, or displayed the same way as the front side, but flipped. It may be understood that while FIG. 3 shows both front and rear images 304 and 306 of the smartphone, in some examples, the heat map 300 may show only the front side or only the rear side, or whichever side is most relevant to the user. For example, if the detection of the source of the magnetic field 308 is stronger on the front side than the rear side, then only the front side may be displayed for the user in the heat map 300. Moreover, in some examples, the heat map 300 may also display the source of the displayed magnetic fields based on related information provided by the detector, e.g., NFC source, cellular source, etc.

As illustrated in FIG. 3, the heat map 300 indicates to the user that the source of the strongest magnetic fields is likely located at the top-left portion of the smartphone (from the front side). In some embodiments, the user may configure the heat map 300 to selectively display and color-code the source of the magnetic field. For example, the rings and lines depicting the magnetic field may be a particular color or shades of color (e.g., red) while magnetic fields corresponding to a different source may represented by a different color or shades of color (e.g., purple). Moreover, it may be understood that various graphics or images may be further overlaid or displayed on the heat map 300. For instance, the word “NFC” or an image of an NFC chip may be displayed in the center of the rings depicting the magnetic field 308.

FIG. 4 illustrates an alternative example of a magnetic field strength mapping system 400 according to embodiments. As shown, the components and the setup of the mapping system 400 is similar to the magnetic field strength mapping system 100 of FIG. 1. A magnetic field strength detector 401 includes at least one or more sensor(s) 402, processing circuitry 404, memory 406, and one or more visual markers 408. Moreover, a computer vision device 411 may include one or more cameras 412, processing circuitry 414, memory 416, and display 418. In FIG. 4, however, the one or more sensors 402 used to detect magnetic field strength may be different or configured differently than the magnetometer used in the system of FIG. 1.

In one example, sensor 402 may be a hall effect sensor. It may be understood that a hall effect sensor may be a device that is used to measure the magnitude of a magnetic field by providing an output voltage that may be directly proportional to the magnetic field strength through the sensor. In another example, the sensor 402 may be a coil, which may be configured similar to the hall effect sensor. Thus, the one or more sensors of the magnetic field strength detector 401 may provide voltage values at various measurement positions or locations on a mobile device. The voltage values may be used to determine respective magnetic field strength measurements at those positions or locations. In some examples, an analog-to-digital converter may be used in conjunction with the coil sensor to measure voltage levels of analog magnetic or NFC oscillations.

Similar to the computer vision device 111 of FIG. 1, the computer vision device 411 may track the one or more visual markers 408 and receive the various magnetic field strength measurements to at least generate a heat map, as described above.

FIG. 5 illustrates an example flow diagram 500 according to one or more embodiments. For example, the flow diagram 500 describes the mapping process corresponding to the magnetic field strength mapping system of FIG. 1. It may be understood that the features associated with the illustrated blocks may be performed or executed by the processing circuitries included in a magnetic field strength detector and computer vision device, such as the ones illustrated in FIG. 1.

At block 502, a magnetic field strength detector (e.g., specifically, a magnetometer of the detector) may be used to measure magnetic field strengths at a first position and a second position relative to an object. For example, the object may be a smartphone, a laptop, a tablet computing device, a wearable computing device, or a mobile computing device. It may be understood that magnetic field strengths at a third position, a fourth position, a fifth position, and so on may also be measured, as described above.

At block 504, the first and second magnetic field strength measurements may be provided to at least one computing device. In examples, the computing device may be a computer vision device. At block 506, one or more images of the object may be captured by at least one camera of the computer vision device. As described above, the image of the object may be used so that a heat map, for example, may be overlaid thereon.

At block 508, the computer vision device may track a visual marker, which may be arranged on the magnetic field strength detector when or as the magnetic field strength measurements are being taken at the first and second locations on the object. At block 510, the first and second magnetic field strength measurements may be received by the computer vision device. Thus, in some examples, blocks 504, 508, and 510 may be performed simultaneously or near simultaneously in real-time since the processes described therein may be related.

At block 512, a magnetic heat map of the object may be generated by the computer vision device or computer vision system based at least in part on the received first and second magnetic field strength measurements. As described above, the heat map may visually indicate the first and second magnetic field strengths (and other measured magnetic field strengths) on the image of the object, which was captured at block 506. In examples, the heat map data may be overlaid on the object image. In examples, the source of the magnetic fields and their associated field strengths may be an NFC reader coil integrated in the object.

It may be understood that the blocks illustrated in FIG. 5 are not limited to any specific order. One or more of the blocks may be performed or executed simultaneously or near simultaneously.

FIG. 6 illustrates an example flow diagram 600 according to one or more embodiments. For example, the flow diagram 600 describes the mapping process corresponding to the magnetic field strength mapping system of FIG. 4, in particular, the processes performed by the magnetic field strength detector. It may be understood that the features associated with the illustrated blocks may be performed or executed by the processing circuitries included in the magnetic field strength detector, such as the one illustrated in FIG. 4.

At block 602, processing circuitry of a magnetic field strength detector may receive, from a sensor, a first voltage value at a first position relative to an object (e.g., a smartphone, laptop, etc.). As described above, the sensor may be a hall effect sensor, or a coil sensor, configured to output a voltage value directly proportional to the magnitude of the magnetic field detected by the sensor. Similarly, at block 604, a second voltage value at a second position relative to the object may be received. Additional voltage values at a third position, a fourth position, a fifth position, and so on may also be received by the detector.

At block 606, a first magnetic field strength measurement at the first position may be determined by the processing circuitry based on the first voltage value. Thus, for instance, if the voltage value at the first position is relatively greater than other voltage values, then it can be determined that the magnitude of a magnetic field is stronger or greater at the first position compared to other locations. Moreover, at block 608, a second magnetic field strength measurement at the second position may be determined based on the second voltage value.

At block 610, the first and second magnetic field strength measurements may be provided to at least one computing device, e.g., a computer vision device, in order to generate a magnetic heat map of the object. The computer vision device, as described above, may track a visual marker arranged on the magnetic field strength detector and match the received field strength measurements to where the visual marker is tracked and located. Based at least on this process, the heat map may be generated and displayed on a display device, where, for example, the various strengths of the magnetic fields may be color-coded, differently shaded, shaped, etc.

It may be understood that the blocks illustrated in FIG. 6 are not limited to any specific order. One or more of the blocks may be performed or executed simultaneously or near simultaneously.

FIG. 7 illustrates an embodiment of an exemplary computing architecture 700, e.g., of a computing device, such as a desktop computer, laptop, tablet computer, mobile computer, smartphone, etc., suitable for implementing various embodiments as previously described. In one embodiment, the computing architecture 700 may include or be implemented as part of a system, which will be further described below. In examples, one or more computing devices and the processing circuitries thereof may be configured to at least run, execute, support, or provide one or more aspects of the magnetic field strength mapping system, such as the computer vision device, and related functionalities. It may be understood that one or more components of the computing architecture 700 may also be implemented or configured in the above-described magnetic field detectors.

As used in this application, the terms “system” and “component” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 700. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers. Further, components may be communicatively coupled to each other by various types of communications media to coordinate operations. The coordination may involve the uni-directional or bi-directional exchange of information. For instance, the components may communicate information in the form of signals communicated over the communications media. The information can be implemented as signals allocated to various signal lines. In such allocations, each message is a signal. Further embodiments, however, may alternatively employ data messages. Such data messages may be sent across various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces.

The computing architecture 700 includes various common computing elements, such as one or more processors, multi-core processors, co-processors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input/output (I/O) components, power supplies, and so forth. The embodiments, however, are not limited to implementation by the computing architecture 700.

As shown in FIG. 7, the computing architecture 700 includes processor 704, a system memory 706 and a system bus 708. The processor 704 can be any of various commercially available processors, processing circuitry, central processing unit (CPU), a dedicated processor, a field-programmable gate array (FPGA), etc.

The system bus 708 provides an interface for system components including, but not limited to, the system memory 706 to the processor 704. The system bus 708 can be any of several types of bus structure that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. Interface adapters may connect to the system bus 708 via slot architecture. Example slot architectures may include without limitation Accelerated Graphics Port (AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA), Micro Channel Architecture (MCA), NuBus, Peripheral Component Interconnect (Extended) (PCI(X)), PCI Express, Personal Computer Memory Card International Association (PCMCIA), and the like.

The computing architecture 700 may include or implement various articles of manufacture. An article of manufacture may include a computer-readable storage medium to store logic. Examples of a computer-readable storage medium may include any tangible media capable of storing electronic data, including volatile memory or non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writeable or re-writeable memory, and so forth. Examples of logic may include executable computer program instructions implemented using any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, object-oriented code, visual code, and the like. Embodiments may also be at least partly implemented as instructions contained in or on a non-transitory computer-readable medium, which may be read and executed by one or more processors to enable performance of the operations described herein.

The system memory 706 may include various types of computer-readable storage media in the form of one or more higher speed memory units, such as read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, ovonic memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, an array of devices such as Redundant Array of Independent Disks (RAID) drives, solid state memory devices (e.g., USB memory, solid state drives (SSD) and any other type of storage media suitable for storing information. In the illustrated embodiment shown in FIG. 7, the system memory 706 can include non-volatile memory 710 and/or volatile memory 712. A basic input/output system (BIOS) can be stored in the non-volatile memory 710.

The computer 702 may include various types of computer-readable storage media in the form of one or more lower speed memory units, including an internal (or external) hard disk drive (HDD) 714, a magnetic floppy disk drive (FDD) 716 to read from or write to a removable magnetic disk 718, and an optical disk drive 720 to read from or write to a removable optical disk 722 (e.g., a CD-ROM or DVD). The HDD 714, FDD 716 and optical disk drive 720 can be connected to the system bus 708 by a HDD interface 724, an FDD interface 726 and an optical drive interface 728, respectively. The HDD interface 724 for external drive implementations can include at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

The drives and associated computer-readable media provide volatile and/or nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For example, a number of program modules can be stored in the drives and memory units 710, 712, including an operating system 730, one or more application programs 732, other program modules 734, and program data 736. In one embodiment, the one or more application programs 732, other program modules 734, and program data 736 can include, for example, the various applications and/or components of the system 800.

A user can enter commands and information into the computer 702 through one or more wire/wireless input devices, for example, a keyboard 738 and a pointing device, such as a mouse 740. Other input devices may include microphones, infra-red (IR) remote controls, radio-frequency (RF) remote controls, game pads, stylus pens, card readers, dongles, finger print readers, gloves, graphics tablets, joysticks, keyboards, retina readers, touch screens (e.g., capacitive, resistive, etc.), trackballs, track pads, sensors, styluses, and the like. These and other input devices are often connected to the processor 704 through an input device interface 742 that is coupled to the system bus 708 but can be connected by other interfaces such as a parallel port, IEEE 1394 serial port, a game port, a USB port, an IR interface, and so forth.

A monitor 744 or other type of display device is also connected to the system bus 708 via an interface, such as a video adaptor 746. The monitor 744 may be internal or external to the computer 702. In addition to the monitor 744, a computer typically includes other peripheral output devices, such as speakers, printers, and so forth.

The computer 702 may operate in a networked environment using logical connections via wire and/or wireless communications to one or more remote computers, such as a remote computer 748. The remote computer 748 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all the elements described relative to the computer 702, although, for purposes of brevity, only a memory/storage device 750 is illustrated. The logical connections depicted include wire/wireless connectivity to a local area network (LAN) 752 and/or larger networks, for example, a wide area network (WAN) 754. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network, for example, the Internet.

When used in a LAN networking environment, the computer 702 is connected to the LAN 752 through a wire and/or wireless communication network interface or adaptor 756. The adaptor 756 can facilitate wire and/or wireless communications to the LAN 752, which may also include a wireless access point disposed thereon for communicating with the wireless functionality of the adaptor 756.

When used in a WAN networking environment, the computer 702 can include a modem 758, or is connected to a communications server on the WAN 754 or has other means for establishing communications over the WAN 754, such as by way of the Internet. The modem 758, which can be internal or external and a wire and/or wireless device, connects to the system bus 708 via the input device interface 742. In a networked environment, program modules depicted relative to the computer 702, or portions thereof, can be stored in the remote memory/storage device 750. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.

The computer 702 is operable to communicate with wire and wireless devices or entities using the IEEE 802 family of standards, such as wireless devices operatively disposed in wireless communication (e.g., IEEE 802.11 over-the-air modulation techniques). This includes at least Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wireless technologies, among others. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. Wi-Fi networks use radio technologies called IEEE 802.118 (a, b, g, n, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wire networks (which use IEEE 802.3-related media and functions).

The various elements of the devices as previously described with reference to FIGS. 1-6 may include various hardware elements, software elements, or a combination of both. Examples of hardware elements may include devices, logic devices, components, processors, microprocessors, circuits, processors, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), memory units, logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. Examples of software elements may include software components, programs, applications, computer programs, application programs, system programs, software development programs, machine programs, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application program interfaces (API), instruction sets, computing code, computer code, code segments, computer code segments, words, values, symbols, or any combination thereof. However, determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints, as desired for a given implementation.

FIG. 8 is a block diagram depicting an exemplary communications architecture 800 suitable for implementing various embodiments. For example, one or more computing devices may communicate with each other via a communications framework, such as a network. At least a first computing device connected to the network may be a computer vision device. At least a second computing device connected to the network may be a different computer vision device. In examples, the first and second computing devices may be part of a larger computer vision system or network.

The communications architecture 800 includes various common communications elements, such as a transmitter, receiver, transceiver, radio, network interface, baseband processor, antenna, amplifiers, filters, power supplies, and so forth. The embodiments, however, are not limited to implementation by the communications architecture 800.

As shown in FIG. 8, the communications architecture 800 includes one or more clients 802 and servers 804. The one or more clients 802 and the servers 804 are operatively connected to one or more respective client data stores 806 and server data stores 807 that can be employed to store information local to the respective clients 802 and servers 804, such as cookies and/or associated contextual information.

The clients 802 and the servers 804 may communicate information between each other using a communication framework 810. The communications framework 810 may implement any well-known communications techniques and protocols. The communications framework 810 may be implemented as a packet-switched network (e.g., public networks such as the Internet, private networks such as an enterprise intranet, and so forth), a circuit-switched network (e.g., the public switched telephone network), or a combination of a packet-switched network and a circuit-switched network (with suitable gateways and translators).

The communications framework 810 may implement various network interfaces arranged to accept, communicate, and connect to a communications network. A network interface may be regarded as a specialized form of an input/output (I/O) interface. Network interfaces may employ connection protocols including without limitation direct connect, Ethernet (e.g., thick, thin, twisted pair 10/100/1000 Base T, and the like), token ring, wireless network interfaces, cellular network interfaces, IEEE 802.7a-x network interfaces, IEEE 802.16 network interfaces, IEEE 802.20 network interfaces, and the like. Further, multiple network interfaces may be used to engage with various communications network types. For example, multiple network interfaces may be employed to allow for the communication over broadcast, multicast, and unicast networks. Should processing requirements dictate a greater amount speed and capacity, distributed network controller architectures may similarly be employed to pool, load balance, and otherwise increase the communicative bandwidth required by clients 802 and the servers 804. A communications network may be any one and the combination of wired and/or wireless networks including without limitation a direct interconnection, a secured custom connection, a private network (e.g., an enterprise intranet), a public network (e.g., the Internet), a Personal Area Network (PAN), a Local Area Network (LAN), a Metropolitan Area Network (MAN), an Operating Missions as Nodes on the Internet (OMNI), a Wide Area Network (WAN), a wireless network, a cellular network, and other communications networks.

The components and features of the devices described above may be implemented using any combination of discrete circuitry, application specific integrated circuits (ASICs), logic gates and/or single chip architectures. Further, the features of the devices may be implemented using microcontrollers, programmable logic arrays and/or microprocessors or any combination of the foregoing where suitably appropriate. It is noted that hardware, firmware and/or software elements may be collectively or individually referred to herein as “logic” or “circuit.”

At least one computer-readable storage medium may include instructions that, when executed, cause a system to perform any of the computer-implemented methods described herein.

Some embodiments may be described using the expression “one embodiment” or “an embodiment” along with their derivatives. These terms mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. Moreover, unless otherwise noted the features described above are recognized to be usable together in any combination. Thus, any features discussed separately may be employed in combination with each other unless it is noted that the features are incompatible with each other.

With general reference to notations and nomenclature used herein, the detailed descriptions herein may be presented in terms of program procedures executed on a computer or network of computers. These procedural descriptions and representations are used by those skilled in the art to most effectively convey the substance of their work to others skilled in the art.

A procedure is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. These operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It proves convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. It should be noted, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to those quantities.

Further, the manipulations performed are often referred to in terms, such as adding or comparing, which are commonly associated with mental operations performed by a human operator. No such capability of a human operator is necessary, or desirable in most cases, in any of the operations described herein, which form part of one or more embodiments. Rather, the operations are machine operations.

Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. These terms are not necessarily intended as synonyms for each other. For example, some embodiments may be described using the terms “connected” and/or “coupled” to indicate that two or more elements are in direct physical or electrical contact with each other. The term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other.

Various embodiments also relate to apparatus or systems for performing these operations. This apparatus may be specially constructed for the required purpose and may be selectively activated or reconfigured by a computer program stored in the computer. The procedures presented herein are not inherently related to a particular computer or other apparatus. The required structure for a variety of these machines will appear from the description given.

It is emphasized that the Abstract of the Disclosure is provided to allow a reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein,” respectively. Moreover, the terms “first,” “second,” “third,” and so forth, are used merely as labels, and are not intended to impose numerical requirements on their objects.

What has been described above includes examples of the disclosed architecture. It is, of course, not possible to describe every conceivable combination of components and/or methodologies, but one of ordinary skill in the art may recognize that many further combinations and permutations are possible. Accordingly, the novel architecture is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. 

What is claimed is:
 1. A method comprising: determining, via one or more processors, a first position of a visual marker relative to an object; receiving a first magnetic field strength measurement corresponding to a first magnetic field strength at the first position; determining, via the one or more processors, a second position of the visual marker relative to the object; receiving a second magnetic field strength measurement corresponding to a second magnetic field strength at the second position; and generating a heat map of the object based at least in part on the received first and second magnetic field strength measurements, and wherein the heat map visually indicates the first and second magnetic field strengths on a digital image of the object.
 2. The method of claim 1, wherein the first and second magnetic field strengths are measured by a sensor, the sensor being a hall effect sensor or a coil.
 3. The method of claim 2, wherein the visual marker is arranged or placed on the sensor.
 4. The method of claim 1, wherein the object is a smartphone, a laptop, a tablet computing device, a wearable computing device, or a mobile computing device.
 5. The method of claim 1, wherein a source of the first and second magnetic field strengths is an NFC reader coil.
 6. The method of claim 1, wherein the visual marker is a shape, a color, a colored shape, a dot, a colored dot, a visual pattern, or machine-readable code.
 7. An apparatus comprising: a sensor; a visual marker; and processing circuitry operable to execute stored instructions that, when executed, cause the processing circuitry to: receive, from the sensor, a first voltage value at a first position relative to an object; receive, from the sensor, a second voltage value at a second position relative to the object; determine a first magnetic field strength measurement at the first position based on the received first voltage value; determine a second magnetic field strength measurement at the second position based on the received second voltage value; and provide the first and second magnetic field strength measurements to at least one computing device, and wherein the visual marker is arranged on the sensor.
 8. The apparatus of claim 7, wherein the sensor is a hall effect sensor or a coil.
 9. The apparatus of claim 7, wherein the object is a smartphone, a laptop, a tablet computing device, a wearable computing device, or a mobile computing device.
 10. The apparatus of claim 7, wherein a source of the first and second magnetic field strength measurements is an NFC reader coil.
 11. The apparatus of claim 7, wherein the visual marker is a shape, a color, a colored shape, a dot, a colored dot, a visual pattern, or machine-readable code.
 12. The apparatus of claim 7, wherein the apparatus has an elongated wand-like shape.
 13. The apparatus of claim 7, wherein the at least one computing device is a computer vision device.
 14. A method comprising: receiving, via a sensor, a first voltage value at a first position relative to an object; receiving, via the sensor, a second voltage value at a second position relative to the object; determining, via processing circuitry, a first magnetic field strength measurement at the first position based on the received first voltage value; determining, via the processing circuitry, a second magnetic field strength measurement at the second position based on the received second voltage value; and providing the first and second magnetic field strength measurements to at least one computing device, and wherein the visual marker is arranged on the sensor.
 15. The method of claim 14, wherein the sensor is a hall effect sensor or a coil.
 16. The method of claim 14, wherein the object is a smartphone, a laptop, a tablet computing device, a wearable computing device, or a mobile computing device.
 17. The method of claim 14, wherein a source of the first and second magnetic field strength measurements is an NFC reader coil.
 18. The method of claim 14, wherein the visual marker is a shape, a color, a colored shape, a dot, a colored dot, a visual pattern, or machine-readable code.
 19. The method of claim 14, wherein the processing circuitry and the sensor are comprised in an apparatus having an elongated wand-like shape.
 20. The method of claim 14, wherein the at least one computing device is a computer vision device. 